Expected Value of Distribution Information for the Newsvendor Problem

Yue, Jiguang; Chen, Bintong; Wang, Min-Chiang

doi:10.1287/opre.1060.0318

Cited by 148 publications

(88 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Closed form solutions for these two cases are provided in both [5] and [3] while other kinds of partial distribution information (i.e. symmetry, unimodality) are also investigated in [3].…”

Section: H(i) Is Chosen As H(µ σ)-All Distributions With the Givenmentioning

confidence: 99%

“…Furthermore, for each order quantity, we calculate the maximum AEVD, the maximum REVD and the cost range. Notice, [5] did not take the ordering cost c into account, hence we enhance their result in Appendix (6.6) when the ordering cost exists, so that we can compare their result with that from our model. The cost range can be computed from the tight upper bound G u (q) and lower bound G l (q) of G f (q) given in [5]:…”

Section: Given Mean µ and Standard Deviation σmentioning

confidence: 99%

“…In fact, the method provided in [5] to compute the min-max AEVD ordering is only for the case of given mean µ and standard deviation σ. And, [3] and Appendix 6.5 in our report deal with the case of given µ and support [A, B], but what we computed is RVAI (Robust Value of Additional Information), which is the difference between the profit and optimal profit, instead of the difference between the costs in AEVD.…”

Section: Given Mean µ and Support [A B]mentioning

confidence: 99%

“…Although these two are equivalent somehow, but we still need to take some time to convert them from one to another. Secondly, neither [5] nor [3] takes the ordering cost c into consideration, hence at the time when we derive Theorem 3 in Appendix 6.5, ordering cost was not taken into account either. But if we need to compare the AEVD with our REVD model, 'c' needs to be taken into account.…”

Section: Given Mean µ and Support [A B]mentioning

confidence: 99%

“…Method 1. Apply the result of Theorem 3 and remark (7) in paper [5]. Let T (γ) be the two-point pdf chosen in Theorem 3 such that q 1 …”

Section: Proof Of Lemmamentioning

confidence: 99%

See 4 more Smart Citations

Newsvendor optimization with limited distribution information

Zhu

Zhang

2013

Optimization Methods and Software

View full text Add to dashboard Cite

We report preliminary results on stochastic optimization with limited distributional information. Lack of complete distribution calls for stochastically robust models that, after exploiting available limited or partial information, offer risk-shielded solutions, i.e., solutions that are insensitive to all possible distributions of random variables. We focus on the well-known newsvendor problem in this study, where the distribution of the random demand is only specified by its mean and one of the following: its standard deviation or its support. We propose a stochastically robust model for the newsvendor problem. More specifically, our model tries to minimize the regret that is defined as the ratio of the expected cost based on limited information to that based on complete information, called Relative Expected Value of Distribution (REVD). We show how to derive an optimal solution to the REVD model. Numerical examples are provided to compare our model with other similar approaches. The goal is to establish a confidence ratio that the decision from our model is not worse, relatively, too much than the decision based on the true distribution which would be never known exactly in real world applications.

show abstract

“…Closed form solutions for these two cases are provided in both [5] and [3] while other kinds of partial distribution information (i.e. symmetry, unimodality) are also investigated in [3].…”

Section: H(i) Is Chosen As H(µ σ)-All Distributions With the Givenmentioning

confidence: 99%

Section: Given Mean µ and Standard Deviation σmentioning

confidence: 99%

Section: Given Mean µ and Support [A B]mentioning

confidence: 99%

Section: Given Mean µ and Support [A B]mentioning

confidence: 99%

“…Method 1. Apply the result of Theorem 3 and remark (7) in paper [5]. Let T (γ) be the two-point pdf chosen in Theorem 3 such that q 1 …”

Section: Proof Of Lemmamentioning

confidence: 99%

See 3 more Smart Citations

Newsvendor optimization with limited distribution information

Zhu

Zhang

2013

Optimization Methods and Software

View full text Add to dashboard Cite

show abstract

Data‐driven distributionally robust optimization of shale gas supply chains under uncertainty

2018

View full text Add to dashboard Cite

This article aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two‐stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first‐stage design decisions, which are related to drilling schedule, pipeline installment, and processing plant construction, as well as second‐stage operational decisions associated with shale gas production, processing, transportation, and distribution. A data‐driven approach is applied to construct the ambiguity set based on principal component analysis and first‐order deviation functions. By taking advantage of affine decision rules, a tractable mixed‐integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a small‐scale illustrative example and a case study of Marcellus shale gas supply chain. Comparisons with alternative optimization models, including the deterministic and stochastic programming counterparts, are investigated as well. © 2018 American Institute of Chemical Engineers AIChE J, 65: 947–963, 2019

show abstract

The loss‐averse newsvendor problem with supply options

Lee

2014

Naval Research Logistics

View full text Add to dashboard Cite

In this article, we consider a loss-averse newsvendor with stochastic demand. The newsvendor might procure options when demand is unknown, and decide how many options to execute only after demand is revealed. If the newsvendor reserves too many options, he would incur high reservation costs. Yet reserving too few could result in lost sales. So the newsvendor faces a trade-off between reservation costs and losing sales. When there are multiple options available, the newsvendor has to consider how many units of each to reserve by studying the trade-off between flexibility and costs. We show how the newsvendor's loss aversion behavior affects his ordering decision, and propose an efficient algorithm to compute his optimal solution in the general case with n options. We also present examples showing how the newsvendor's ordering strategy changes as loss aversion rises.

show abstract

Expected Value of Distribution Information for the Newsvendor Problem

Cited by 148 publications

References 10 publications

Newsvendor optimization with limited distribution information

Newsvendor optimization with limited distribution information

Data‐driven distributionally robust optimization of shale gas supply chains under uncertainty

The loss‐averse newsvendor problem with supply options

Contact Info

Product

Resources

About